China’s container freight prices on the global behavior of inflation rate after COVID-19

Manuel Monge; Ana Lazcano; Luis A. Gil-Alana

doi:10.24294/jipd.v8i11.7407

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China’s container freight prices on the global behavior of inflation rate after COVID-19

Manuel Monge

Ana Lazcano

Luis A. Gil-Alana

Journal of Infrastructure Policy and Development 2024, 8(11), 7407; https://doi.org/10.24294/jipd.v8i11.7407

Submitted：24 Jun 2024

Accepted：24 Jul 2024

Published：08 Oct 2024

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Abstract

The main objective of this article is to analyze the relationship between increases in freight costs and inflation in the markets due to the increases reflected in the prices of the products in some economies in destination ports such as the United States, Europe, Japan, South Africa, the United Arab Emirates, New Zealand and South Korea. We use fractionally integrated methods and Granger causality test to calculate the correlation between these indicators. The results indicate that, after a significant drop in inflation in 2020, probably due to the confinement caused by the pandemic, the increases observed in inflation and freight costs are expected to be transitory given their stationary behavior. We also find a close correlation between both indicators in Europe, the United States and South Africa.

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References

Aguiar-Conraria, L., & Soares, M. J. (2011). Oil and the macroeconomy: using wavelets to analyze old issues. Empirical Economics, 40(3), 645-655.

Aguiar‐Conraria, L., & Soares, M. J. (2014). The continuous wavelet transform: Moving beyond uni‐and bivariate analysis. Journal of Economic Surveys, 28(2), 344-375.

Aguiar-Conraria, L., Azevedo, N., & Soares, M. J. (2008). Using wavelets to decompose the time-frequency effects of monetary policy. Physica A: Statistical mechanics and its applications, 387(12), 2863-2878.

Akaike, H. (1973). Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika, 60(2), 255–265. https://doi.org/10.1093/biomet/60.2.255

Akaike, H. (1979). A Bayesian extension of the minimum AIC procedure of autoregressive model fitting. Biometrika, 66(2), 237–242. https://doi.org/10.1093/biomet/66.2.237

Akbulaev, N., & Bayramli, G. (2020). Maritime transport and economic growth: Interconnection and influence (an example of the countriesin the Caspian sea coast; Russia, Azerbaijan, Turkmenistan, Kazakhstan and Iran). Marine Policy, 118, 104005. https://doi.org/10.1016/j.marpol.2020.104005

Aye, S. A., & Heyns, P. S. (2017). An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission. Mechanical Systems and Signal Processing, 84, 485-498.

Banomyong, R. (2005). The impact of port and trade security initiatives on maritime supply-chain management. Maritime Policy & Management, 32(1), 3–13. https://doi.org/10.1080/0308883042000326102

Baruník, J., & Dvořáková, S. (2015). An empirical model of fractionally cointegrated daily high and low stock market prices. Economic Modelling 45, 193-206.

Başer, S., & Açık, A. (2018). Stock Market as an Indicator of Maritime Transport Demand: An Evidence from Turkey and ISTFIX Region. Kastamonu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 20(4), 77-88.

Beran, J. (1998). On unified model selection for stationary and nonstationary short- and long-memory autoregressive processes. Biometrika, 85(4), 921–934. https://doi.org/10.1093/biomet/85.4.921

Bottasso, A., Conti, M., Ferrari, C., et al. (2013). The impact of port throughput on local employment: Evidence from a panel of European regions. Transport Policy, 27, 32–38. https://doi.org/10.1016/j.tranpol.2012.12.001

Carrière-Swallow, Y., Deb, P., Furceri, D., et al. (2023). Shipping costs and inflation. Journal of International Money and Finance, 130, 102771. https://doi.org/10.1016/j.jimonfin.2022.102771

Cheng, T. C. E., Zanjirani Farahani, R., Lai, K., et al. (2015). Sustainability in maritime supply chains: Challenges and opportunities for theory and practice. Transportation Research Part E: Logistics and Transportation Review, 78, 1–2. https://doi.org/10.1016/j.tre.2015.03.007

Cullinane, K., & Haralambides, H. (2021). Global trends in maritime and port economics: the COVID-19 pandemic and beyond. Maritime Economics & Logistics, 23(3), 369–380. https://doi.org/10.1057/s41278-021-00196-5

Deng, P., Lu, S., & Xiao, H. (2013). Evaluation of the relevance measure between ports and regional economy using structural equation modeling. Transport Policy, 27, 123–133. https://doi.org/10.1016/j.tranpol.2013.01.008

Dewandaru, G., Masih, R., & Masih, A. M. M. (2016). Contagion and interdependence across Asia-Pacific equity markets: An analysis based on multi-horizon discrete and continuous wavelet transformations. International Review of Economics & Finance, 43, 363–377. https://doi.org/10.1016/j.iref.2016.01.002

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74(366), 427. https://doi.org/10.2307/2286348

Diebold, F. X., & Rudebusch, G. D. (1991). On the power of Dickey-Fuller tests against fractional alternatives, Economics Letters, 35, 155-160. https://doi.org/10.1016/0165-1765(91)90163-F

Dolatabadi, S., Narayan, P. K., Nielsen, M. Ø., et al. (2017). Economic significance of commodity return forecasts from the fractionally cointegrated VAR model. Journal of Futures Markets, 38(2), 219–242. https://doi.org/10.1002/fut.21866

Dolatabadi, S., Nielsen, M. Ø., & Xu, K. (2014). A Fractionally Cointegrated VAR Analysis of Price Discovery in Commodity Futures Markets. Journal of Futures Markets, 35(4), 339–356. https://doi.org/10.1002/fut.21693

Dun, & Bradstreet. (2022). Russia-Ukraine crisis: Implications for the global economy and businesses. Available online: https://www.dnb.co.uk/perspectives/russia-ukraine-impacts-global-supply-chain.html (accessed on 10 May 2024).

Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient Tests for an Autoregressive Unit Root. Econometrica, 64(4), 813. https://doi.org/10.2307/2171846

Fawcett, S. E., & Magnan, G. M. (2004). Ten guiding principles for high-impact SCM. Business Horizons, 47(5), 67–74. https://doi.org/10.1016/j.bushor.2004.07.011

Fuller, W. A. (1976). Introduction to Statistical Time Series. New York: JohnWiley.

Geweke, J., & Porter‐Hudak, S. (1983). The Estimation and Application of Long Memory Time Series Models. Journal of Time Series Analysis, 4(4), 221–238. https://doi.org/10.1111/j.1467-9892.1983.tb00371.x

Gil-Alana, L. A., & Carcel, H. (2020). A fractional cointegration var analysis of exchange rate dynamics. The North American Journal of Economics and Finance, 51, 100848. https://doi.org/10.1016/j.najef.2018.09.006

Granger, C. (1981), Some properties of time series data and their use in econometric model specification. Journal of Econometrics, 16(1), 121-130. https://doi.org/10.1016/0304-4076(81)90079-8

Granger, C. W. (1980). Long memory relationships and the aggregation of dynamic models. Journal of Econometrics, 14(2), 227-238. https://doi.org/10.1016/0304-4076(80)90092-5

Granger, C. W. J., Joyeux, R. (1980). An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis, 1, 15-29.

Hassler, U., Wolters, J. (1994). On the power of unit root tests against fractional alternatives, Economics Letters, 45(1), 1-5. https://doi.org/10.1016/0165-1765(94)90049-3

Heilig, L., Schwarze, S., & Voss, S. (2017). An Analysis of Digital Transformation in the History and Future of Modern Ports. In: Proceedings of the 50th Hawaii International Conference on System Sciences.

Herriford, T., Johnson, E., Sly, N., & Lee Smith, A. (2016). How Does a Rise in International Shipping Costs Affect U.S. Inflation? Macro Bulletin, Federal Reserve Bank of Kansas City, 1-3.

Hosking, J. R. M. (1981). Fractional differencing. Biometrika, 68(1), 165–176. https://doi.org/10.1093/biomet/68.1.165

Jammazi, R., Ferrer, R., Jareño, F., et al. (2017). Time-varying causality between crude oil and stock markets: What can we learn from a multiscale perspective? International Review of Economics & Finance, 49, 453–483. https://doi.org/10.1016/j.iref.2017.03.007

Johansen, S. (1995). Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press.

Johansen, S. (2008). A Representation Theory for A Class of Vector Autoregressive Models for Fractional Processes. Econometric Theory, 24(3), 651–676. https://doi.org/10.1017/s0266466608080274

Johansen, S., & Nielsen, M. O. (2010). Likelihood inference for a nonstationary fractional autoregressive model. Journal of Econometrics, 158, 51-66.

Johansen, S., & Nielsen, M. O. (2012). Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model. Econometrica, 80(6), 2667–2732. https://doi.org/10.3982/ecta9299

Johansen, S., & Nielsen, M. O. (2014). The role of initial values in nonstationary fractional time series models. QED Working Paper 1300, Queen’s University.

Jones, M. E. C., Nielsen, M. O., & Popiel, M. K. (2014). A fractionally cointegrated VAR analysis of economic voting and political support. Canadian Journal of Economics/Revue Canadienne d’économique, 47(4), 1078–1130. https://doi.org/10.1111/caje.12115

Khan, K., & Derindere Köseoğlu, S. (2020). Is palladium price in bubble? Resources Policy, 68, 101780. https://doi.org/10.1016/j.resourpol.2020.101780

Khurshid, A., Khan, K., Chen, Y., et al. (2023). Do green transport and mitigation technologies drive OECD countries to sustainable path? Transportation Research Part D: Transport and Environment, 118, 103669. https://doi.org/10.1016/j.trd.2023.103669

Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 65(3), 159-178.

Lee, D., & Schmidt, P. (1996), On the power of the KPSS test of stationarity against fractionally-integrated alternatives. Journal of Econometrics, 73(1), 285-302. https://doi.org/10.1016/0304-4076(95)01741-0

Maciel, L. (2017). Technical analysis based on high and low stock prices forecasts: Evidence for Brazil using a fractionally cointegrated VAR model. Empirical Economics, 58(4), 1513-1540.

McFarlane, D., Giannikas, V., & Lu, W. (2016). Intelligent logistics: Involving the customer. Computers in Industry, 81, 105–115. https://doi.org/10.1016/j.compind.2015.10.002

Menegaki, A. N. (2019). The ARDL Method in the Energy-Growth Nexus Field; Best Implementation Strategies. Economies, 7(4), 105. https://doi.org/10.3390/economies7040105

Mentzer, J. T., DeWitt, W., Keebler, J. S., et al. (2001). Defining Supply Chain Management. Journal of Business Logistics, 22(2), 1–25. https://doi.org/10.1002/j.2158-1592.2001.tb00001.x

Mlambo, C. (2021). The Impact of Port Performance on Trade: The Case of Selected African States. Economies, 9(4), 135. https://doi.org/10.3390/economies9040135

Molaris, V. A., Triantafyllopoulos, K., Papadakis, G., et al. (2021). The Effect of COVID-19 on minor dry bulk shipping: A Bayesian time series and a neural networks approach. Communications in Statistics: Case Studies, Data Analysis and Applications, 7(4), 624–638. https://doi.org/10.1080/23737484.2021.1979434

Monge, M., & Cristóbal, E. (2021). Terrorism and the behavior of oil production and prices in OPEC. Resources Policy, 74, 102321. https://doi.org/10.1016/j.resourpol.2021.102321

Monge, M., & Gil-Alana, L. A. (2021). Lithium industry and the U.S. crude oil prices. A fractional cointegration VAR and a Continuous Wavelet Transform analysis. Resources Policy, 72, 102040. https://doi.org/10.1016/j.resourpol.2021.102040

Ng, S., & Perron, P. (2001). LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power. Econometrica, 69(6), 1519–1554. https://doi.org/10.1111/1468-0262.00256

Nielsen, M. Ø., & Popiel, M. K. (2018). A Matlab program and user’s guide for the fractionally cointegrated VAR model. Ontario, Canada, K7L 3N6.

Oliveira, J. B., Lima, R. S., & Montevechi, J. A. B. (2016). Perspectives and relationships in Supply Chain Simulation: A systematic literature review. Simulation Modelling Practice and Theory, 62, 166–191. https://doi.org/10.1016/j.simpat.2016.02.001

Phillips, P. C. B. (1987). Time Series Regression with a Unit Root. Econometrica, 55(2), 277. https://doi.org/10.2307/1913237

Phillips, P. C. B. (1999). Discrete Fourier transforms of fractional processes. Department of Economics, University of Auckland.

Phillips, P. C. B. (2007). Unit root log periodogram regression. Journal of Econometrics, 138(1), 104–124. https://doi.org/10.1016/j.jeconom.2006.05.017

Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335

Rashid Khan, H. U., Siddique, M., Zaman, K., et al. (2018). The impact of air transportation, railways transportation, and port container traffic on energy demand, customs duty, and economic growth: Evidence from a panel of low, middle, and high income countries. Journal of Air Transport Management, 70, 18–35. https://doi.org/10.1016/j.jairtraman.2018.04.013

Robinson, P. M. (1994). Efficient Tests of Nonstationary Hypotheses. Journal of the American Statistical Association, 89(428), 1420–1437. https://doi.org/10.1080/01621459.1994.10476881

Robinson, P. M. (1995a). Gaussian Semiparametric Estimation of Long Range Dependence. The Annals of Statistics, 23(5). https://doi.org/10.1214/aos/1176324317

Robinson, P. M. (1995b). Log-Periodogram Regression of Time Series with Long Range Dependence. The Annals of Statistics, 23(3). https://doi.org/10.1214/aos/1176324636

Sánchez, R. J., Hoffmann, J., Micco, A., et al. (2003). Port Efficiency and International Trade: Port Efficiency as a Determinant of Maritime Transport Costs. Maritime Economics & Logistics, 5(2), 199–218. https://doi.org/10.1057/palgrave.mel.9100073

Shan, J., Yu, M., & Lee, C. Y. (2014). An empirical investigation of the seaport’s economic impact: Evidence from major ports in China. Transportation Research Part E: Logistics and Transportation Review, 69, 41–53. https://doi.org/10.1016/j.tre.2014.05.010

Smialek, J., & Nelson, E. (2021). The world’s top central bankers see supply chain problems prolonging inflation. Available online: https://www.nytimes.com/2021/09/29/business/central-bankers-supplychains-inflation.html (accessed on 13 May 2024).

Sowell, F. (1992). Maximum likelihood estimation of stationary univariate fractionally integrated time series models. Journal of Econometrics 53(1-3), 165-188. https://doi.org/10.1016/0304-4076(92)90084-5

Stiglitz, J. E. (1990). Symposium on Bubbles. Journal of Economic Perspectives, 4(2), 13–18. https://doi.org/10.1257/jep.4.2.13

Swanson, A. (2022). The world’s top central bankers see supply chain problems prolonging inflation. Available online: https://www.nytimes.com/2022/01/12/business/inflation-supply-chain.html (accessed on 15 May 2024).

Thomson Reuters. (2023). China Containerized Freight Index. Refinitiv Eikon.

Tiwari, A. K., Mutascu, M. I., & Albulescu, C. T. (2016). Continuous wavelet transform and rolling correlation of European stock markets. International Review of Economics & Finance, 42, 237–256. https://doi.org/10.1016/j.iref.2015.12.002

UNCTAD. (2015). COVID-19 and Maritime Transport Impact and Responses. UNCTAD.

Vacha, L., & Barunik, J. (2012). Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis. Energy Economics, 34(1), 241-247.

Wong, C. Y., Boon‐itt, S., & Wong, C. W. Y. (2011). The contingency effects of environmental uncertainty on the relationship between supply chain integration and operational performance. Journal of Operations Management, 29(6), 604–615. https://doi.org/10.1016/j.jom.2011.01.003

Xu, L., Shi, J., Chen, J., et al. (2021). Estimating the effect of COVID-19 epidemic on shipping trade: An empirical analysis using panel data. Marine Policy, 133, 104768. https://doi.org/10.1016/j.marpol.2021.104768

Yang, Y., Liu, Q., & Chang, C. H. (2023). China-Europe freight transportation under the first wave of COVID-19 pandemic and government restriction measures. Research in Transportation Economics, 97, 101251. https://doi.org/10.1016/j.retrec.2022.101251

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